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Abstract:

The disclosure describes systems and methods for modeling relationships
between entities on a network using data collected from a plurality of
communication channels including social data, spatial data, temporal data
and logical data within a W4 Network. The W4 Network personalizes and
automates sorting, filtering and processing of W4COMN communications
delivered or requested to be delivered using personalized value-based
ranking and encoding of data, which is modeled from the point-of-view
(POV) of any specific user, topic or node in the W4 Distributed graph.
POV modeling supplies comparative value services to users which entails
individuated data models to be aggregated and used in customization and
personalization forecasting for each user and their associated data
management needs.

Claims:

1-23. (canceled)

24. A method comprising: identifying, using a computing device, a first
real world entity (RWE) and a second RWE having at least one
relationship; identifying, using the computing device, a first
information object (IO) associated with the first RWE that defines a
state of the first RWE, and a second information object associated with
the first RWE that defines a state of a second RWE; and identifying,
using the computing device, a third information object associated with
the second RWE that defines a state of the second RWE, and a fourth
information object associated with the second RWE that defines a state of
the first RWE, such that: in response to a request for state information
about the first RWE from a perspective of the second RWE, analyzing,
using the computing device, the fourth information object.

25. The method of claim 24 further comprising: in response to a request
for state information about the second RWE from a perspective of the
first RWE, analyzing, using the computing device, the second information
object.

26. The method of claim 25 further comprising: in response to a request
for state information about the first RWE from a perspective of the first
RWE, analyzing, using the computing device, the first information object.

27. The method of claim 26 further comprising: in response to a request
for state information about the second RWE from a perspective of the
second RWE, analyzing, using the computing device, the third information
object.

28. The method of claim 27 further comprising: generating, using the
computing device, an entity information object for each the analysis of
the first, second, third and fourth information objects such that each
generated entity information object defines a point-of-view (POV)
relationship between each of the information objects associated with the
first RWE and the second RWE.

29. The method of claim 28 further comprising storing, using the
computing device, each POV relationship based upon a respective
perspective of the first RWE to the second RWE and the second RWE to the
first RWE.

30. The method of claim 28 further comprising identifying the POV
relationship as explicit if the POV relationship between the first RWE
and the second RWE is based upon an explicit declaration of the POV
relationship by at least one of the first RWE and the second RWE.

31. The method of claim 28 further comprising identifying, using the
computing device, the POV relationship as implicit if determined based on
modeling of known interactions between the first RWE and the second RWE.

32. The method of claim 31 such that the modeling comprises a probability
analysis of data contained in electronic communications between the first
RWE and the second RWE.

33. The method of claim 31 such that the modeling comprises analysis of
content of electronic communications between the first RWE and the second
RWE.

34. The method of claim 31 such that the entity information objects
represent the first RWE and the second RWE, the entity information
objects comprise corresponding descriptors and identifiers for the first
RWE and the second RWE.

35. The method of claim 31 further comprising determining, using the
computing device, the implicit POV relationship based upon stored social
data, spatial data, temporal data and logical data of the first RWE and
the second RWE and the information objects associated with the first RWE
and second RWE.

37. The method of claim 28 further comprising storing each POV
relationship independently.

38. A non-transitory computer-readable storage medium for tangibly
storing thereon computer readable instructions for a method comprising:
identifying a first real world entity (RWE) and a second RWE having at
least one relationship; identifying a first information object (IO)
associated with the first RWE that defines a state of the first RWE, and
a second information object associated with the first RWE that defines a
state of a second RWE; and identifying a third information object
associated with the second RWE that defines a state of the second RWE,
and a fourth information object associated with the second RWE that
defines a state of the first RWE, such that: in response to a request for
state information about the first RWE from a perspective of the second
RWE, analyzing the fourth information object.

39. The non-transitory computer-readable storage medium of claim 38
further comprising: in response to a request for state information about
the second RWE from a perspective of the first RWE, analyzing the second
information object.

40. The non-transitory computer-readable storage medium of claim 39
further comprising: in response to a request for state information about
the first RWE from a perspective of the first RWE, analyzing the first
information object.

41. The non-transitory computer-readable storage medium of claim 41
further comprising: in response to a request for state information about
the second RWE from a perspective of the second RWE, analyzing the third
information object.

42. The method of claim 41 further comprising: generating, using the
computing device, an entity information object for each the analysis of
the first, second, third and fourth information objects such that each
generated entity information object defines a point-of-view (POV)
relationship between each of the information objects associated with the
first RWE and the second RWE.

43. A computing device comprising: a processor; a storage medium for
tangibly storing thereon program logic for execution by the processor,
the program logic comprising: logic executed by the processor for
identifying a first real world entity (RWE) and a second RWE having at
least one relationship; logic executed by the processor for identifying a
first information object (IO) associated with the first RWE that defines
a state of the first RWE, and a second information object associated with
the first RWE that defines a state of a second RWE; and logic executed by
the processor for identifying a third information object associated with
the second RWE that defines a state of the second RWE, and a fourth
information object associated with the second RWE that defines a state of
the first RWE, such that: in response to a request for state information
about the first RWE from a perspective of the second RWE, analyzing the
fourth information object.

Description:

BACKGROUND

[0001] A great deal of information is generated when people use electronic
devices, such as when people use mobile phones and cable set-top boxes.
Such information, such as location, applications used, social network,
physical and online locations visited, to name a few, could be used to
deliver useful services and information to end users, and provide
commercial opportunities to advertisers and retailers. However, most of
this information is effectively abandoned due to deficiencies in the way
such information can be captured. For example, and with respect to a
mobile phone, information is generally not gathered while the mobile
phone is idle (i.e., not being used by a user). Other information, such
as presence of others in the immediate vicinity, time and frequency of
messages to other users, and activities of a user's social network are
also not captured effectively.

SUMMARY

[0002] This disclosure describes systems and methods for using data
collected and stored by multiple devices on a network in order to improve
the performance of the services provided via the network. In particular,
the disclosure describes systems and methods for identifying related
communications and storing information about those communications into a
single information object (IO), instead of storing a separate IO for each
communication. In one application, the systems and methods can be used to
dynamically identify and describe events based on information received
from independent communications over disparate communication channels by
correlating information known about each communication, its sender and
its recipient(s).

[0003] One aspect of the disclosure is a method for modeling relationships
comprising identifying a first real world entity (RWE) having a first
information object (IO) associated therewith that defines a state of the
first RWE, and a second information object associated with the first RWE
that defines a state of a second RWE. A third information object
associated with the second RWE is identified that defines a state of the
second RWE. A fourth information object associated with the second RWE is
also identified that defines a state of the first RWE. In response to a
request for state information about the first RWE from a perspective of
the second RWE, the fourth information object is analyzed. The second
information object is analyzed in response to a request for state
information about the second RWE from a perspective of the first RWE. The
first information object is analyzed in response to a request for state
information about the first RWE from a perspective of the first RWE, and
in response to a request for state information about the second RWE from
a perspective of the second RWE the third information object is also
analyzed.

[0004] Another aspect of the disclosure is a computer-readable medium
encoding instructions for performing a method for modeling relationships
comprising identifying a first real world entity (RWE) having a first
information object (IO) associated therewith that defines a state of the
first RWE, and a second information object associated with the first RWE
that defines a state of a second RWE. A third information object
associated with the second RWE is identified that defines a state of the
second RWE. A fourth information object associated with the second RWE is
also identified that defines a state of the first RWE. In response to a
request for state information about the first RWE from a perspective of
the second RWE, the fourth information object is analyzed. The second
information object is analyzed in response to a request for state
information about the second RWE from a perspective of the first RWE. The
first information object is analyzed in response to a request for state
information about the first RWE from a perspective of the first RWE, and
in response to a request for state information about the second RWE from
a perspective of the second RWE the third information object is also
analyzed.

[0005] In yet another aspect, the disclosure describes a system for
modeling relationships comprising a correlation engine connected via at
least one communication channel to a plurality of computing devices
transmitting information objects (IOs) over the at least one
communication channel. Computer-readable media is connected to the
correlation engine which stores social data, spatial data, temporal data
and logical data associated with a plurality of real-world entities
(RWEs) including the plurality of computing devices. A point-of-view
(POV) relationship identification engine identifies relationships of the
RWEs via determinations based upon analyses of W4 data that imply POV
relationships in view of correlations made by the correlation engine.

[0006] These and various other features as well as advantages will be
apparent from a reading of the following detailed description and a
review of the associated drawings. Additional features are set forth in
the description that follows and, in part, will be apparent from the
description, or can be learned by practice of the described embodiments.
The benefits and features will be realized and attained by the structure
particularly pointed out in the written description and claims hereof as
well as the appended drawings.

[0007] It is to be understood that both the foregoing general description
and the following detailed description are exemplary and explanatory and
are intended to provide further explanation of the disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following drawing figures, which form a part of this
application, are illustrative of embodiments systems and methods
described below and are not meant to limit the scope of the disclosure in
any manner, which scope shall be based on the claims appended hereto.

[0009]FIG. 1 illustrates an example of the relationships between RWEs and
IOs on the W4 COMN.

[0010]FIG. 2 illustrates an example of metadata defining the
relationships between RWEs and IOs on the W4 COMN.

[0017]FIG. 9 illustrates some of the elements in a W4 engine adapted to
optimize the storage of data derived from communications over disparate
communication channels.

[0018]FIG. 10 illustrates an embodiment of a method for identifying,
storing and using point of view relationships

DETAILED DESCRIPTION

[0019] This disclosure describes a communication network, referred herein
as the "W4 Communications Network" or W4 COMN, that uses information
related to the "Who, What, When and Where" of interactions with the
network to provide improved services to the network's users. The W4 COMN
is a collection of users, devices and processes that foster both
synchronous and asynchronous communications between users and their
proxies. It includes an instrumented network of sensors providing data
recognition and collection in real-world environments about any subject,
location, user or combination thereof.

[0020] As a communication network, the W4 COMN handles the
routing/addressing, scheduling, filtering, prioritization, replying,
forwarding, storing, deleting, privacy, transacting, triggering of a new
message, propagating changes, transcoding and linking. Furthermore, these
actions can be performed on any communication channel accessible by the
W4 COMN.

[0021] The W4 COMN uses a data modeling strategy for creating profiles for
not only users and locations but also any device on the network and any
kind of user-defined data with user-specified conditions from a rich set
of possibilities. Using Social, Spatial, Temporal and Logical data
available about a specific user, topic or logical data object, every
entity known to the W4 COMN can be mapped and represented against all
other known entities and data objects in order to create both a micro
graph for every entity as well as a global graph that interrelates all
known entities against each other and their attributed relations.

[0022] In order to describe the operation of the W4 COMN, two elements
upon which the W4 COMN is built must first be introduced, real-world
entities and information objects. These distinction are made in order to
enable correlations to be made from which relationships between
electronic/logical objects and real objects can be determined. A
real-world entity (RWE) refers to a person, device, location, or other
physical thing known to the W4 COMN. Each RWE known to the W4 COMN is
assigned or otherwise provided with a unique W4 identification number
that absolutely identifies the RWE within the W4 COMN.

[0023] RWEs can interact with the network directly or through proxies,
which can themselves be RWEs. Examples of RWEs that interact directly
with the W4 COMN include any device such as a sensor, motor, or other
piece of hardware that connects to the W4 COMN in order to receive or
transmit data or control signals. Because the W4 COMN can be adapted to
use any and all types of data communication, the devices that can be RWEs
include all devices that can serve as network nodes or generate, request
and/or consume data in a networked environment or that can be controlled
via the network. Such devices include any kind of "dumb" device
purpose-designed to interact with a network (e.g., cell phones, cable
television set top boxes, fax machines, telephones, and radio frequency
identification (RFID) tags, sensors, etc.). Typically, such devices are
primarily hardware and their operations can not be considered separately
from the physical device.

[0024] Examples of RWEs that must use proxies to interact with W4 COMN
network include all non-electronic entities including physical entities,
such as people, locations (e.g., states, cities, houses, buildings,
airports, roads, etc.) and things (e.g., animals, pets, livestock,
gardens, physical objects, cars, airplanes, works of art, etc.), and
intangible entities such as business entities, legal entities, groups of
people or sports teams. In addition, "smart" devices (e.g., computing
devices such as smart phones, smart set top boxes, smart cars that
support communication with other devices or networks, laptop computers,
personal computers, server computers, satellites, etc.) are also
considered RWEs that must use proxies to interact with the network. Smart
devices are electronic devices that can execute software via an internal
processor in order to interact with a network. For smart devices, it is
actually the executing software application(s) that interact with the W4
COMN and serve as the devices' proxies.

[0025] The W4 COMN allows associations between RWEs to be determined and
tracked. For example, a given user (an RWE) can be associated with any
number and type of other RWEs including other people, cell phones, smart
credit cards, personal data assistants, email and other communication
service accounts, networked computers, smart appliances, set top boxes
and receivers for cable television and other media services, and any
other networked device. This association can be made explicitly by the
user, such as when the RWE is installed into the W4 COMM. An example of
this is the set up of a new cell phone, cable television service or email
account in which a user explicitly identifies an RWE (e.g., the user's
phone for the cell phone service, the user's set top box and/or a
location for cable service, or a username and password for the online
service) as being directly associated with the user. This explicit
association can include the user identifying a specific relationship
between the user and the RWE (e.g., this is my device, this is ray home
appliance, this person is my friend/father/son/etc., this device is
shared between me and other users, etc.). RWEs can also be implicitly
associated with a user based on a current situation. For example, a
weather sensor on the W4 COMN can be implicitly associated with a user
based on information indicating that the user lives or is passing near
the sensor's location.

[0026] An information object (IO), on the other hand, is a logical object
that stores, maintains, generates, serves as a source for or otherwise
provides data for use by RWEs and/or the W4 COMN. IOs are distinct from
RWEs in that IOs represent data, whereas RWEs can create or consume data
(often by creating or consuming IOs) during their interaction with the W4
COMN. Examples of IOs include passive objects such as communication
signals (e.g., digital and analog telephone signals, streaming media and
interprocess communications), email messages, transaction records,
virtual cards, event records (e.g., a data file identifying a time,
possibly in combination with one or more RWEs such as users and
locations, that can further be associated with a known
topic/activity/significance such as a concert, rally, meeting, sporting
event, etc.), recordings of phone calls, calendar entries, web pages,
database entries, electronic media objects (e.g., media files containing
songs, videos, pictures, images, audio messages, phone calls, etc.),
electronic files and associated metadata.

[0027] In addition, IOs include any executing process or application that
consumes or generates data such as an email communication application
(such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaring
application, a word processing application, an image editing application,
a media player application, a weather monitoring application, a browser
application and a web page server application. Such active IOs can or can
not serve as a proxy for one or more RWEs. For example, voice
communication software on a smart phone can serve as the proxy for both
the smart phone and for the owner of the smart phone.

[0028] An IO in the W4 COMN can be provided a unique W4 identification
number that absolutely identifies the IO within the W4 COMN. Although
data in an IO can be revised by the act of an RWE, the IO remains a
passive, logical data representation or data source and, thus, is not an
RWE.

[0029] For every IO there are at least three classes of associated RWEs.
The first is the RWE who owns or controls the IO, whether as the creator
or a rights holder (e.g., an RWE with editing rights or use rights to the
IO). The second is the RWE(s) that the IO relates to, for example by
containing information about the RWE or that identifies the RWE. The
third are any RWEs who then pay any attention (directly or through a
proxy process) to the IO, in which "paying attention" refers to accessing
the IO in order to obtain data from the IO for some purpose.

[0030] "Available data" and "W4 data" means data that exists in an IO in
some form somewhere or data that can be collected as needed from a known
IO or RWE such as a deployed sensor. "Sensor" means any source of W4 data
including PCs, phones, portable PCs or other wireless devices, household
devices, cars, appliances, security scanners, video surveillance, RFID
tags in clothes, products and locations, online data or any other source
of information about a real-world user/topic/thing (RWE) or logic-based
agent/process/topic/thing (IO).

[0031]FIG. 1 illustrates an example of the relationships between RWEs and
IOs on the W4 COMN. In the embodiment illustrated, a user 102 is a RWE of
the network provided with a unique network ID. The user 102 is a human
that communicates with the network via the proxy devices 104, 106, 108,
110 associated with the user 102, all of which are RWEs of the network
and provided with their own unique network ID. Some of these proxies can
communicate directly with the W4 COMN or can communicate with the W4 COMN
via IOs such as applications executed on or by the device.

[0032] As mentioned above the proxy devices 104, 106, 108, 110 can be
explicitly associated with the user 102. For example, one device 104 can
be a smart phone connected by a cellular service provider to the network
and another device 106 can be a smart vehicle that is connected to the
network. Other devices can be implicitly associated with the user 102.
For example, one device 108 can be a "dumb" weather sensor at a location
matching the current location of the user's cell phone 104, and thus
implicitly associated with the user 102 while the two RWEs 104, 108 are
co-located. Another implicitly associated device 110 can be a sensor 110
for physical location 112 known to the W4 COMN. The location 112 is
known, either explicitly (through a user-designated relationship, e.g.,
this is my home, place of employment, parent, etc.) or implicitly (the
user 102 is often co-located with the RWE 112 as evidenced by data from
the sensor 110 at that location 112), to be associated with the first
user 102.

[0033] The user 102 can also be directly associated with other people,
such as the person 140 shown, and then indirectly associated with other
people 142, 144 through their associations as shown. Again, such
associations can be explicit (e.g., the user 102 can have identified the
associated person 140 as his/her father, or can have identified the
person 140 as a member of the user's social network) or implicit (e.g.,
they share the same address).

[0034] Tracking the associations between people (and other RWEs as well)
allows the creation of the concept of "intimacy": Intimacy being a
measure of the degree of association between two people or RWEs. For
example, each degree of removal between RWEs can be considered a lower
level of intimacy, and assigned lower intimacy score. Intimacy can be
based solely on explicit social data or can be expanded to include all W4
data including spatial data and temporal data.

[0035] Each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144 of the W4 COMN
can be associated with one or more IOs as shown. Continuing the examples
discussed above, FIG. 1 illustrates two IQs 122, 124 as associated with
the cell phone device 104. One IO 122 can be a passive data object such
as an event record that is used by scheduling/calendaring software on the
cell phone, a contact IO used by an address book application, a
historical record of a transaction made using the device 104 or a copy of
a message sent from the device 104. The other IO 124 can be an active
software process or application that serves as the device's proxy to the
W4 COMN by transmitting or receiving data via the W4 COMN. Voice
communication software, scheduling/calendaring software, an address book
application or a text messaging application are all examples of IOs that
can communicate with other IOs and RWEs on the network. The IOs 122, 124
can be locally stored on the device 104 or stored remotely on some node
or datastore accessible to the W4 COMN, such as a message server or cell
phone service datacenter. The IO 126 associated with the vehicle 108 can
be an electronic file containing the specifications and/or current status
of the vehicle 108, such as make, model, identification number, current
location, current speed, current condition, current owner, etc. The IO
128 associated with sensor 108 can identify the current state of the
subject(s) monitored by the sensor 108, such as current weather or
current traffic. The IO 130 associated with the cell phone 110 can be
information in a database identifying recent calls or the amount of
charges on the current bill.

[0036] Furthermore, those RWEs which can only interact with the W4 COMN
through proxies, such as the people 102, 140, 142, 144, computing devices
104, 106 and location 112, can have one or more IOs 132, 134, 146, 148,
150 directly associated with them. An example includes IOs 132, 134 that
contain contact and other RWE-specific information. For example, a
person's IO 132, 146, 148, 150 can be a user profile containing email
addresses, telephone numbers, physical addresses, user preferences,
identification of devices and other RWEs associated with the user,
records of the user's past interactions with other RWE's on the W4 COMN
(e.g., transaction records, copies of messages, listings of time and
location combinations recording the user's whereabouts in the past), the
unique W4 COMN identifier for the location and/or any relationship
information (e.g., explicit user-designations of the user's relationships
with relatives, employers, co-workers, neighbors, service providers,
etc.). Another example of a person's IO 132, 146, 148, 150 includes
remote applications through which a person can communicate with the W4
COMN such as an account with a web-based email service such as Yahoo!
Mail. The location's IO 134 can contain information such as the exact
coordinates of the location, driving directions to the location, a
classification of the location (residence, place of business, public,
non-public, etc.), information about the services or products that can be
obtained at the location, the unique W4 COMN identifier for the location,
businesses located at the location, photographs of the location, etc.

[0037] In order to correlate RWEs and IOs to identify relationships, the
W4 COMN makes extensive use of existing metadata and generates additional
metadata where necessary. Metadata is loosely defined as data that
describes data. For example, given an IO such as a music file, the core,
primary or object data of the music file is the actual music data that is
converted by a media player into audio that is heard by the listener.
Metadata for the same music file can include data identifying the artist,
song, etc., album art, and the format of the music data. This metadata
can be stored as part of the music file or in one or more different IOs
that are associated with the music file or both. In addition, W4 metadata
for the same music file can include the owner of the music file and the
rights the owner has in the music file. As another example, if the IO is
a picture taken by an electronic camera, the picture can include in
addition to the primary image data from which an image can be created on
a display, metadata identifying when the picture was taken, where the
camera was when the picture was taken, what camera took the picture, who,
if anyone, is associated (e.g., designated as the camera's owner) with
the camera, and who and what are the subjects of/in the picture. The W4
COMN uses all the available metadata in order to identify implicit and
explicit associations between entities and data objects.

[0038]FIG. 2 illustrates an example of metadata defining the
relationships between RWEs and IOs on the W4 COMN. In the embodiment
shown, an IO 202 includes object data 204 and five discrete items of
metadata 206, 208, 210, 212, 214. Some items of metadata 208, 210, 212
can contain information related only to the object data 204 and unrelated
to any other IO or RWE. For example, a creation date, text or an image
that is to be associated with the object data 204 of the IO 202.

[0039] Some of items of metadata 206, 214, on the other hand, can identify
relationships between the IO 202 and other RWEs and IOs. As illustrated,
the IO 202 is associated by one item of metadata 206 with an RWE 220 that
RWE 220 is further associated with two IQs 224, 226 and a second RWE 222
based on some information known to the W4 COMN. This part of FIG. 2, for
example, could describe the relations between a picture (IO 202)
containing metadata 206 that identifies the electronic camera (the first
RWE 220) and the user (the second RWE 224) that is known by the system to
be the owner of the camera 220. Such ownership information can be
determined, for example, from one or another of the IOs 224, 226
associated with the camera 220.

[0040]FIG. 2 also illustrates metadata 214 that associates the IO 202
with another IO 230. This IO 230 is itself associated with three other
IOs 232, 234, 236 that are further associated with different RWEs 242,
244, 246. This part of FIG. 2, for example, could describe the relations
between a music file (IO 202) containing metadata 206 that identifies the
digital rights file (the first IO 230) that defines the scope of the
rights of use associated with this music file 202. The other IOs 232,
234, 236 are other music files that are associated with the rights of use
and which are currently associated with specific owners (RWEs 242, 244,
246).

[0041]FIG. 3 illustrates a conceptual model of the W4 COMN. The W4 COMN
300 creates an instrumented messaging infrastructure in the form of a
global logical network cloud conceptually sub-divided into
networked-clouds for each of the 4Ws: Who, Where, What and When. In the
Who cloud 302 are all users whether acting as senders, receivers, data
points or confirmation/certification sources as well as user proxies in
the forms of user-program processes, devices, agents, calendars, etc. In
the Where cloud 304 are all physical locations, events, sensors or other
RWEs associated with a spatial reference point or location. The When
cloud 306 is composed of natural temporal events (that is events that are
not associated with particular location or person such as days, times,
seasons) as well as collective user temporal events (holidays,
anniversaries, elections, etc.) and user-defined temporal events
(birthdays, smart-timing programs). The What cloud 308 is comprised of
all known data--web or private, commercial or user--accessible to the W4
COMN, including for example environmental data like weather and news,
RWE-generated data, IOs and IO data, user data, models, processes and
applications. Thus, conceptually, most data is contained in the What
cloud 308.

[0042] As this is just a conceptual model, it should be noted that some
entities, sensors or data will naturally exist in multiple clouds either
disparate in time or simultaneously. Additionally, some IOs and RWEs can
be composites in that they combine elements from one or more clouds. Such
composites can be classified or not as appropriate to facilitate the
determination of associations between RWEs and IOs. For example, an event
consisting of a location and time could be equally classified within the
When cloud 306, the What cloud 308 and/or the Where cloud 304.

[0043] The W4 engine 310 is center of the W4 COMN's central intelligence
for making all decisions in the W4 COMN. An "engine" as referred to
herein is meant to describe a software, hardware or firmware (or
combinations thereof) system, process or functionality that performs or
facilitates the processes, features and/or functions described herein
(with or without human interaction or augmentation). The W4 engine 310
controls all interactions between each layer of the W4 COMN and is
responsible for executing any approved user or application objective
enabled by W4 COMN operations or interoperating applications. In an
embodiment, the W4 COMN is an open platform upon which anyone can write
an application. To support this, it includes standard published APIs for
requesting (among other things) synchronization, disambiguation, user or
topic addressing, access rights, prioritization or other value-based
ranking, smart scheduling, automation and topical, social, spatial or
temporal alerts.

[0044] One function of the W4 COMN is to collect data concerning all
communications and interactions conducted via the W4 COMN, which can
include storing copies of IOs and information identifying all RWEs and
other information related to the IOs (e.g., who, what, when, where
information). Other data collected by the W4 COMN can include information
about the status of any given RWE and IO at any given time, such as the
location, operational state, monitored conditions (e.g., for an RWE that
is a weather sensor, the current weather conditions being monitored or
for an RWE that is a cell phone, its current location based on the
cellular towers it is in contact with) and current status.

[0045] The W4 engine 310 is also responsible for identifying RWEs and
relationships between RWEs and IOs from the data and communication
streams passing through the W4 COMN. The function of identifying RWEs
associated with or implicated by IOs and actions performed by other RWEs
is referred to as entity extraction. Entity extraction includes both
simple actions, such as identifying the sender and receivers of a
particular IO, and more complicated analyses of the data collected by
and/or available to the W4 COMN, for example determining that a message
listed the time and location of an upcoming event and associating that
event with the sender and receiver(s) of the message based on the context
of the message or determining that an RWE is stuck in a traffic jam based
on a correlation of the RWE's location with the status of a co-located
traffic monitor.

[0046] It should be noted that when performing entity extraction from an
IO, the IO can be an opaque object with only W4 metadata related to the
object (e.g., date of creation, owner, recipient, transmitting and
receiving RWEs, type of IO, etc.), but no knowledge of the internals of
the IO (i.e., the actual primary or object data contained within the
object). Knowing the content of the IO does not prevent W4 data about the
IO (or RWE) to be gathered. The content of the IO if known can also be
used in entity extraction, if available, but regardless of the data
available entity extraction is performed by the network based on the
available data. Likewise, W4 data extracted around the object can be used
to imply attributes about the object itself, while in other embodiments,
full access to the IO is possible and RWEs can thus also be extracted by
analyzing the content of the object, e.g. strings within an email are
extracted and associated as RWEs to for use in determining the
relationships between the sender, user, topic or other RWE or IO impacted
by the object or process.

[0047] In an embodiment, the W4 engine 310 represents a group of
applications executing on one or more computing devices that are nodes of
the W4 COMN. For the purposes of this disclosure, a computing device is a
device that includes a processor and memory for storing data and
executing software (e.g., applications) that perform the functions
described. Computing devices can be provided with operating systems that
allow the execution of software applications in order to manipulate data.

[0048] In the embodiment shown, the W4 engine 310 can be one or a group of
distributed computing devices, such as a general-purpose personal
computers (PCs) or purpose built server computers, connected to the W4
COMN by suitable communication hardware and/or software. Such computing
devices can be a single device or a group of devices acting together.
Computing devices can be provided with any number of program modules and
data files stored in a local or remote mass storage device and local
memory (e.g., RAM) of the computing device. For example, as mentioned
above, a computing device can include an operating system suitable for
controlling the operation of a networked computer, such as the WINDOWS XP
or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.

[0049] Some RWEs can also be computing devices such as smart phones,
web-enabled appliances, PCs, laptop computers, and personal data
assistants (PDAs). Computing devices can be connected to one or more
communications networks such as the Internet, a publicly switched
telephone network, a cellular telephone network, a satellite
communication network, a wired communication network such as a cable
television or private area network. Computing devices can be connected
any such network via a wired data connection or wireless connection such
as a a WiMAX (802.36), a Bluetooth or a cellular telephone connection.

[0050] Local data structures, including discrete IOs, can be stored on a
mass storage device (not shown) that is connected to, or part of, any of
the computing devices described herein including the W4 engine 310. For
example, in an embodiment, the data backbone of the W4 COMM, discussed
below, includes multiple mass storage devices that maintain the IOs,
metadata and data necessary to determine relationships between RWEs and
IOs as described herein. A mass storage device includes some form of
computer-readable media and provides non-volatile storage of data and
software for retrieval and later use by one or more computing devices.
Although the description of computer-readable media contained herein
refers to a mass storage device, such as a hard disk or CD-ROM drive, it
should be appreciated by those skilled in the art that computer-readable
media can be any available media that can be accessed by a computing
device.

[0051] By way of example, and not limitation, computer-readable media can
comprise computer storage media and communication media. Computer storage
media include volatile and non-volatile, removable and non-removable
media implemented in any method or technology for storage of information
such as computer-readable instructions, data structures, program modules
or other data. Computer storage media includes, but is not limited to,
RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory
technology, CD-ROM, DVD, or other optical storage, magnetic cassette,
magnetic tape, magnetic disk storage or other magnetic storage devices,
or any other medium which can be used to store the desired information
and which can be accessed by the computer.

[0052]FIG. 4 illustrates the functional layers of the W4 COMN
architecture. At the lowest layer, referred to as the sensor layer 402,
is the network 404 of the actual devices, users, nodes and other RWEs.
The instrumentation of the network nodes to utilize them as sensors
include known technologies like web analytics, GPS, cell-tower pings, use
logs, credit card transactions, online purchases, explicit user profiles
and implicit user profiling achieved through behavioral targeting, search
analysis and other analytics models used to optimize specific network
applications or functions.

[0053] The next layer is the data layer 406 in which the data produced by
the sensor layer 402 is stored and cataloged. The data can be managed by
either the network 404 of sensors or the network infrastructure 406 that
is built on top of the instrumented network of users, devices, agents,
locations, processes and sensors. The network infrastructure 408 is the
core under-the-covers network infrastructure that includes the hardware
and software necessary to receive that transmit data from the sensors,
devices, etc. of the network 404. It further includes the processing and
storage capability necessary to meaningfully categorize and track the
data created by the network 404.

[0054] The next layer of the W4 COMN is the user profiling layer 410. This
layer 410 can further be distributed between the network infrastructure
408 and user applications/processes 412 executing on the W4 engine or
disparate user computing devices. The user profiling layer 410 performs
the W4 COMN's user profiling functions. Personalization is enabled across
any single or combination of communication channels and modes including
email, IM, texting (SMS, etc.), photobloging, audio (e.g. telephone
call), video (teleconferencing, live broadcast), games, data confidence
processes, security, certification or any other W4 COMM process call for
available data.

[0055] In one embodiment, the user profiling layer 410 is a logic-based
layer above all sensors to which sensor data are sent in the rawest form
to be mapped and placed into the W4 COMN data backbone 420. The data
(collected and refined, related and deduplicated, synchronized and
disambiguated) are then stored in one or a collection of related
databases available to all processes of all applications approved on the
W4 COMN. All Network-originating actions and communications are based
upon the fields of the data backbone, and some of these actions are such
that they themselves become records somewhere in the backbone, e.g.
invoicing, while others, e.g. fraud detection, synchronization,
disambiguation, can be done without an impact to profiles and models
within the backbone.

[0056] Actions originating from anything other than the network, e.g.,
RWEs such as users, locations, proxies and processes, come from the
applications layer 414 of the W4 COMN. Some applications can be developed
by the W4 COMN operator and appear to be implemented as part of the
communications infrastructure 408, e.g. email or calendar programs
because of how closely they operate with the sensor processing and user
profiling layer 410. The applications 412 also serve some role as a
sensor in that they, through their actions, generate data back to the
data layer 406 via the data backbone concerning any data created or
available due to the applications execution.

[0057] The applications layer 414 also provides a personalized user
interface (UI) based upon device, network, carrier as well as
user-selected or security-based customizations. Any UI can operate within
the W4 COMN if it is instrumented to provide data on user interactions or
actions back to the network. This is a basic sensor function of any W4
COMN application/UI, and although the W4 COMN can interoperate with
applications/UIs that are not instrumented, it is only in a delivery
capacity and those applications/UIs would not be able to provide any data
(let alone the rich data otherwise available from W4-enabled devices.)

[0058] In the case of W4 COMN mobile devices, the UI can also be used to
confirm or disambiguate incomplete W4 data in real-time, as well as
correlation, triangulation and synchronization sensors for other nearby
enabled or non-enabled devices. At some point, the network effects of
enough enabled devices allow the network to gather complete or nearly
complete data (sufficient for profiling and tracking) of a non-enabled
device because of its regular intersection and sensing by enabled devices
in its real-world location.

[0059] Above the applications layer 414 (and sometimes hosted within it)
is the communications delivery network(s) 416. This can be operated by
the W4 COMN operator or be independent third-party carrier service, but
in either case it functions to deliver the data via synchronous or
asynchronous communication. In every case, the communication delivery
network 414 will be sending or receiving data (e.g., http or IP packets)
on behalf of a specific application or network infrastructure 408
request.

[0060] The communication delivery layer 418 also has elements that act as
sensors including W4 entity extraction from phone calls, emails, blogs,
etc. as well as specific user commands within the delivery network
context, e.g., "save and prioritize this call" said before end of call
can trigger a recording of the previous conversation to be saved and for
the W4 entities within the conversation to analyzed and increased in
weighting prioritization decisions in the personalization/user profiling
layer 410.

[0061]FIG. 5 illustrates an embodiment of analysis components of a W4
engine as shown in FIG. 3. As discussed above, the W4 Engine is
responsible for identifying RWEs and relationships between RWEs and IOs
from the data and communication streams passing through the W4 COMN.

[0062] In one embodiment the W4 engine connects, interoperates and
instruments all network participants through a series of sub-engines that
perform different operations in the entity extraction process. One such
sub-engine is an attribution engine 504. The attribution engine 504
tracks the real-world ownership, control, publishing or other conditional
rights of any RWE in any IO. Whenever a new IO is detected by the W4
engine 502, e.g., through creation or transmission of a new message, a
new transaction record, a new image file, etc., ownership is assigned to
the IO. The attribution engine 504 creates this ownership information and
further allows this information to be determined for each IO known to the
W4 COMN.

[0063] The W4 engine 502 further includes a correlation engine 506. The
correlation engine 506 operates in two capacities: first, to identify
associated RWEs and IOs and their relationships (such as by creating a
combined graph of any combination of RWEs and IOs and their attributes,
relationships and reputations within contexts or situations) and second,
as a sensor analytics pre-processor for attention events from any
internal or external source.

[0064] In one embodiment, the identification of associated RWEs and IOs
function of the correlation engine 506 is done by graphing the available
data. In this embodiment, a histogram of all RWEs and IOs is created,
from which correlations based on the graph can be made. Graphing, or the
act of creating a histogram, is a computer science method of identifying
a distribution of data in order to identify relevant information and make
correlations between the data. In a more general mathematical sense, a
histogram is simply a mapping mi that counts the number of
observations that fall into various disjoint categories (known as bins),
whereas the graph of a histogram is merely one way to represent a
histogram. By selecting each IO, RWE, and other known parameters (e.g.,
times, dates, locations, etc.) as different bins and mapping the
available data, relationships between RWEs, IOs and the other parameters
can be identified.

[0065] As a pre-processor, the correlation engine 506 monitors the
information provided by RWEs in order to determine if any conditions are
identified that can trigger an action on the part of the W4 engine 502.
For example, if a delivery condition has been associated with a message,
when the correlation engine 506 determines that the condition is met, it
can transmit the appropriate trigger information to the W4 engine 502
that triggers delivery of the message.

[0066] The attention engine 508 instruments all appropriate network nodes,
clouds, users, applications or any combination thereof and includes close
interaction with both the correlation engine 506 and the attribution
engine 504.

[0067]FIG. 6 illustrates an embodiment of a W4 engine showing different
components within the sub-engines described generally above with
reference to FIG. 4. In one embodiment the W4 engine 602 includes an
attention engine 608, attribution engine 604 and correlation engine 606
with several sub-managers based upon basic function.

[0068] The attention engine 608 includes a message intake and generation
manager 610 as well as a message delivery manager 612 that work closely
with both a message matching manager 614 and a real-time communications
manager 616 to deliver and instrument all communications across the W4
COMN.

[0069] The attribution engine 604 works within the user profile manager
618 and in conjunction with all other modules to identify, process/verify
and represent ownership and rights information related to RWEs, IOs and
combinations thereof.

[0070] The correlation engine 606 dumps data from both of its channels
(sensors and processes) into the same data backbone 620 which is
organized and controlled by the W4 analytics manager 622 and includes
both aggregated and individualized archived versions of data from all
network operations including user logs 624, attention rank place logs
626, web indices and environmental logs 618, e-commerce and financial
transaction information 630, search indexes and logs 632, sponsor content
or conditionals, ad copy and any and all other data used in any W4COMN
process, IO or event. Because of the amount of data that the W4 COMN will
potentially store, the data backbone 620 includes numerous database
servers and datastores in communication with the W4 COMN to provide
sufficient storage capacity.

[0071] As discussed above, the data collected by the W4 COMN includes
spatial data, temporal data, RWE interaction data, IO content data (e.g.,
media data), and user data including explicitly-provided and deduced
social and relationship data. Spatial data can be any data identifying a
location associated with an RWE. For example, the spatial data can
include any passively collected location data, such as cell tower data,
global packet radio service (GPRS) data, global positioning service (GPS)
data, WI-FI data, personal area network data, IP address data and data
from other network access points, or actively collected location data,
such as location data entered by the user.

[0072] Temporal data is time based data (e.g., time stamps) that relate to
specific times and/or events associated with a user and/or the electronic
device. For example, the temporal data can be passively collected time
data (e.g., time data from a clock resident on the electronic device, or
time data from a network clock), or the temporal data can be actively
collected time data, such as time data entered by the user of the
electronic device (e.g., a user maintained calendar).

[0073] The interaction data can be any data associated with user
interaction of the electronic device, whether active or passive. Examples
of interaction data include interpersonal communication data, media data,
relationship data, transactional data and device interaction data, all of
which are described in further detail below. Table 1, below, is a
non-exhaustive list including examples of electronic data.

[0074] With respect to the interaction data, communications between any
RWEs can generate communication data that is transferred via the W4 COMN.
For example, the communication data can be any data associated with an
incoming or outgoing short message service (SMS) message, email message,
voice call (e.g., a cell phone call, a voice over IP call), or other type
of interpersonal communication relative to an RWE, such as information
regarding who is sending and receiving the communication(s). As described
above, communication data can be correlated with, for example, temporal
data to deduce information regarding frequency of communications,
including concentrated communication patterns, which can indicate user
activity information.

[0075] Logical and IO data refers to the data contained by an IO as well
as data associated with the IO such as creation time, owner, associated
RWEs, when the IO was last accessed, etc. If the IO is a media object,
the term media data can be used. Media data can include any data relating
to presentable media, such as audio data, visual data, and audiovisual
data. For example, the audio data can be data relating to downloaded
music, such as genre, artist, album and the like, and includes data
regarding ringtones, ringbacks, media purchased, playlists, and media
shared, to name a few. The visual data can be data relating to images
and/or text received by the electronic device (e.g., via the Internet or
other network). The visual data can be data relating to images and/or
text sent from and/or captured at the electronic device. The audiovisual
data can be data associated with any videos captured at, downloaded to,
or otherwise associated with the electronic device. The media data
includes media presented to the user via a network, such as use of the
Internet, and includes data relating to text entered and/or received by
the user using the network (e.g., search terms), and interaction with the
network media, such as click data (e.g., advertisement banner clicks,
bookmarks, click patterns and the like). Thus, the media data can include
data relating to the user's RSS feeds, subscriptions, group memberships,
game services, alerts, and the like. The media data also includes
non-network activity, such as image capture and/or video capture using an
electronic device, such as a mobile phone. The image data can include
metadata added by the user, or other data associated with the image, such
as, with respect to photos, location when the photos were taken,
direction of the shot, content of the shot, and time of day, to name a
few. As described in further detail below, media data can be used, for
example, to deduce activities information or preferences information,
such as cultural and/or buying preferences information.

[0076] The relationship data can include data relating to the
relationships of an RWE or IO to another RWE or IO. For example, the
relationship data can include user identity data, such as gender, age,
race, name, social security number, photographs and other information
associated with the user's identity. User identity information can also
include e-mail addresses, login names and passwords. Relationship data
can further include data identifying explicitly associated RWEs. For
example, relationship data for a cell phone can indicate the user that
owns the cell phone and the company that provides the service to the
phone. As another example, relationship data for a smart car can identify
the owner, a credit card associated with the owner for payment of
electronic tolls, those users permitted to drive the car and the service
station for the car.

[0077] Relationship data can also include social network data. Social
network data includes data relating to any relationship that is
explicitly defined by a user or other RWE, such as data relating to a
user's friends, family, co-workers, business relations, and the like.
Social network data can include, for example, data corresponding with a
user-maintained electronic address book. Relationship data can be
correlated with, for example, location data to deduce social network
information, such as primary relationships (e.g., user-spouse,
user-children and user-parent relationships) or other relationships
(e.g., user-friends, user-co-worker, user-business associate
relationships). Relationship data also can be utilized to deduce, for
example, activities information.

[0078] The interaction data can also include transactional data. The
transactional data can be any data associated with commercial
transactions undertaken by or at the mobile electronic device, such as
vendor information, financial institution information (e.g., bank
information), financial account information (e.g., credit card
information), merchandise information and costs/prices information, and
purchase frequency information, to name a few. The transactional data can
be utilized, for example, to deduce activities and preferences
information. The transactional information can also be used to deduce
types of devices and/or services the user owns and/or in which the user
can have an interest.

[0079] The interaction data can also include device or other RWE
interaction data. Such data includes both data generated by interactions
between a user and a RWE on the W4 COMN and interactions between the RWE
and the W4 COMN. RWE interaction data can be any data relating to an
RWE's interaction with the electronic device not included in any of the
above categories, such as habitual patterns associated with use of an
electronic device data of other modules/applications, such as data
regarding which applications are used on an electronic device and how
often and when those applications are used. As described in further
detail below, device interaction data can be correlated with other data
to deduce information regarding user activities and patterns associated
therewith. Table 2, below, is a non-exhaustive list including examples of
interaction data.

TABLE-US-00002
TABLE 2
Examples of Interaction Data
Type of Data Example(s)
Interpersonal Text-based communications, such as SMS
communication and e-mail
data Audio-based communications, such as voice
calls, voice notes, voice mail
Media-based communications, such as
multimedia messaging service (MMS)
communications
Unique identifiers associated with a
communication, such as phone numbers, e-mail
addresses, and network addresses
Media data Audio data, such as music data (artist, genre,
track, album, etc.)
Visual data, such as any text, images and
video data, including Internet data, picture data,
podcast data and playlist data
Network interaction data, such as click
patterns and channel viewing patterns
Relationship data User identifying information, such as name,
age, gender, race, and social security number
Social network data
Transactional data Vendors
Financial accounts, such as credit cards and
banks data
Type of merchandise/services purchased
Cost of purchases
Inventory of purchases
Device interaction Any data not captured above dealing with
data user interaction of the device, such as patterns of use
of the device, applications utilized, and so forth

Point-of-View Modeling Based on W4 Data

[0080] Many of the W4 processes and functions are based on the modeling of
relationships between RWEs. In an embodiment, the W4 data are processed
and analyzed using data models that treat data not as abstract signals
stored in databases, but rather as IOs that represent or contain
information about RWEs that actually exist, have existed, or will exist
in real space, real time, and are real people, objects, places, times,
and/or events. As such, the data model for W4 IOs that represent W4 RWEs
(Where/When/Who/What) will model not only the signals recorded from the
RWEs or about the RWEs, but also represent these RWEs and their
interactions in ways that model the affordances and constraints of
entities and activities in the physical world. A notable aspect is the
modeling of data about RWEs as embodied and situated in real world
contexts so that the computation of similarity, clustering, distance, and
inference take into account the states, or state information, and actions
of RWEs in the real world and the contexts and patterns of these states
and actions.

[0081] With appropriate data models for IOs that represent data from or
about RWEs, a variety of machine learning techniques can be applied to
analyze the W4 data. In an embodiment, W4 data may be modeled as a
"feature vector" in which the vector includes not only raw sensed data
from or about W4 RWEs, but also higher order features that account for
the contextual and periodic patterns of the states and action of W4 RWEs.
Each of these features in the feature vector may have a numeric or
symbolic value that can be compared for similarity to other numeric or
symbolic values in a feature space. Each feature may also be modeled with
an additional value from 0 to 1 (a certainty value) to represent the
probability that the feature is true. By modeling W4 data about RWEs in
ways that account for the affordances and constraints of their context
and patterns in the physical world in features and higher order features
with or without certainty values, this data (whether represented in
feature vectors or by other data modeling techniques) can then be
processed to determine similarity, difference, clustering, hierarchical
and graph relationships, as well as inferential relationships among the
features and feature vectors.

[0082] A wide variety of statistical and machine learning techniques can
be applied to W4 data from simple histograms to Sparse Factor Analysis
(SFA), Hidden Markov Models (HMMs), Support Vector Machines (SVMs),
Bayesian Methods, etc. Such learning algorithms may be populated with
data models that contain features and higher order features represent not
just the "content" of the signals stored as IOs, e.g., the raw W4 data,
but also model the contexts and patterns of the RWEs that exist, have
existed, or will exist in the physical world from which these data have
been captured.

[0083] One aspect of the W4 COMN is the ability to identify relationships
between RWEs that are different depending upon the point-of-view (POV)
from which the relationship is considered, such as for example a
father-son relationship between two people or a master-slave relationship
between two devices. POV relationships are useful to the W4 COMN as in
many cases the identification of relationships is made relative to a
specific POV of an RWE. For example, the W4 COMN may generate a histogram
from the W4 data representing a first user's interaction with the world
of known RWEs to determine which RWEs are relevant and important to the
user. Such an analysis is inherently POV oriented as, for example, an
email from a first user discussing the first user's opinions about
another known RWE or a transaction record that the first user made a
purchase at an RWE provide different information depending on the POV of
each RWE.

[0084] While it is true that many types of relationships are symmetrical
and that the relationship is the same between each related entity (e.g.
friendship) in many cases relationships are different depending on the
point of view from which the relationship is considered. As mentioned
above, the parent-child relationship is a classic example of a
relationship between two people, in which depending on the point of view
the relationship is different. The W4 COMN tracks these relationships by
allowing for a point of view modeling system when defining relationships
for RWEs. In an embodiment, for each RWE known to the system, one or more
information objects may be created in order to describe that RWE. In
essence, such an IO is the data that represents the RWE to the W4 COMN.
Such an IO will be referred to as an entity IO in order to distinguish it
from other IOs which contain data representing things such as
communications and other data on the W4 COMN. In an embodiment, an entity
IO will contain such information as the unique identifier for the RWE,
other information known about the RWE such as what type of entity it is
(e.g. whether it is a location, person, device, event, etc.). In
addition, an entity IO may include a listing of known proxies for the
entity as well as a listing of RWEs that the subject RWE is a proxy for.
For example, an entity IO for a person "John Smith" may include such
information as Mr. Smith's unique W4 identifier, a listing of Mr. Smith's
known devices based on correlations of W4 data, for example Mr. Smith's
cell phone, Mr. Smith's laptop computer, Mr. Smith's car, Mr. Smith's
home telephone, etc. In addition, Mr. Smith's entity IO may include a
listing of user accounts such as email accounts, log-ins, accounts at
different social networking pages, WebPages, etc. which are known to be
associated with or proxies for Mr. Smith on the network. Such an entity
IO may further include such information as explicit information provided
by Mr. Smith, either directly to the W4 COMN or via one of Mr. Smith's
proxy RWEs or proxy IOs. It should be noted that in an alternative
embodiment, information in an entity IO may be stored in the IO, or
alternatively, the entity IO may contain pointers directly to other IOs
that contain the information.

[0085] One aspect of point of view modeling is to identify relationships
that are point of view relationships in an entity IO. In an embodiment,
depending upon the type of RWE (e.g. person, location, business, etc.)
certain standard point of view relationships may be accounted for within
the entity IO. For example, for every RWE that is a person, it is
presumed that that person will generally have a father and a mother, and
that therefore there are two other RWEs which may or may not be known to
the system and may or may not be identifiable based on those RWE's
interaction with the system, but which the system explicitly knows will
exist. In addition, W4 COMN will know that the relationship between the
given user and the user's parents will be point of view relationships,
and the entity IO for every user may include data elements reserved for
information identifying the father IO and the mother IO. Likewise data
elements may be provided for children, for spouses and for other
potential point of view relationships that can be reasonably attributed
to every person who may be identified by the system. Other entity types
may also have anticipated or expected point of view relationships. For
example, a businesses may have an employer-employee relationship with its
employees, and thus entity IOs for businesses may be provided with data
elements specifically for that point of view relationship. Depending on
the type of business there may be directors, shareholders, and other
potential point of view relationships associated within the entity IO.

[0086] As discussed above, relationships may be identified either
explicitly or implicitly from an analysis of the W4 data. An explicit
declaration of a relationship refers to, for example, a specific user
identifying another user as being related in a specific manner. For
example, when providing background information at the request of the W4
COMN or some other communication channel (e.g. when setting up an
account) a new user may identify members of the family and the exact
relationship of those members. This is considered explicit identification
of a relationship. If that relationship is a point of view relationship,
then this information may be stored into an entity IO for that entity.
Furthermore, if the other entities with whom the first entity has a
relationship are known to the system, their entity IOs may also be
updated to identify the relationship. Such an updating may be performed
automatically and will be performed on the basis of the point of view
relationship. In many cases, if the point of view relationship is known
from the point of view of one party, the reciprocal point of view
relationship from the point of view of the other party is explicitly
defined and can be automatically added to the other party's entity IO.
For example, if a first user identifies a second user as his son, then
the system can return and identify the first user as the father of the
second user automatically. Such reciprocal identifications may be
performed automatically, may be performed by notifying the second user in
order to obtain a verification of this explicit identification, or the
reciprocal point of view relationship may be ignored until there is an
explicit identification received from the second user.

[0087] In the absence of an explicit identification of a point of view
relationship based on correlations identified by the W4 COMN and other
relationships identified from an analysis of the W4 data, point of view
relationships may be determined to a high degree of probability and even
explicitly determined from communications that are not themselves
explicit declarations to the W4 COMN. For example, a first user may send
an email to a second user via the W4 COMN in which the salutation "Dear
Dad" or "Dear Father" is provided. Based on one or more detections of
this salutation or version of similar salutations which indicate that the
second user is the father of the first user it may be determined by the
modeling used in the W4 analysis of the W4 data to a high probability
that the second user is the father of the first user. Other examples of
such actions detected by the W4 COMN which may be considered to imply a
point of view relationship include such things as purchases and
communications and transmissions sent on holidays associated with point
of view transmissions such as Secretaries Day, Mothers Day, Fathers Day,
etc. In addition the content of various communications such as the
content of an email as described above may be inspected and analyzed in
order to identify point of view relationships.

[0088]FIG. 7 illustrates a conceptual embodiment of relationships between
entity IOs. FIG. 7 illustrates two IOs, 702, 704. The first IO 702 is an
entity IO representing a first RWE, such as for example, a user. The
second IO 704 represents another entity ID, such as for example, another
user. In the embodiment illustrated, entity IO number one has a point of
view relationship with entity IO number one as illustrated by the arrow
706. Such an entity relationship may be that entity IO number two
represented by IO 704 is the father of entity IO number one represented
by the IO 702. Likewise there is a reciprocal relationship 708
illustrated by the line 708 which in this case illustrates that from the
perspective of entity TO number two, entity IO number two is the son of
entity IO number one. Two other relationships are illustrated by two
arrows 710 and 712. These are point of view relationships in which the
entity provides the entities own perception of itself. For example, the
first entity may consider itself to be a good father or a mountain biker,
or a good boss, or a good driver, or a resource for people in a specific
industry. This is illustrated by the arrow 712. By defining a point of
view relationship in which an entity's own point of view itself can be
defined, it is possible to allow entities to provide additional
information about themselves. However, by defining such information as a
point of view relationship of itself, the system can then obtain
additional information based on other relationship information derived
from other sources and verify or support an entity's view of itself. FIG.
7 illustrates a simple embodiment that includes only two entities. A more
complicated embodiment in which each entity is related by multiple point
of view relationships to many other entities within a network creates a
mode and link model that can be easily searched and used for data mining.

[0089]FIG. 8 illustrates an embodiment of a method for modeling
relationships based on an analysis of data contained in communications
transmitted via different communication channels using social, temporal,
spatial and topical data for entities on a network. In the embodiment
described below, depending on how the architecture is implemented, the
operations described can be performed by one or more of the various
engines described above. In addition, sub-engines can be created and used
to perform specific operations in order to improve the network's
performance or provide specific POV-related services.

[0090] As described above, an aspect of the W4 COMN that allows for data
optimization is the ongoing collection of W4 data from the RWEs
interacting with the network. In an embodiment, this collection is an
independent operation 899 of the W4 COMN and thus current W4 social,
temporal, spatial and topical data are always available for use in data
optimization. In addition, part of this data collection operation 899
includes the determination of ownership and the association of different
RWEs with different IOs as described above, as well as the identification
of new RWEs when they first interact with the W4 COMN. Therefore, each IO
is owned/controlled by at least one RWE with a known unique identifier on
the W4 COMN, and each IO can have many other associations with other RWEs
that are known to the W4 COMN.

[0091] Provided with the W4 data that was collected in the data collection
operation 899, the method 800 starts with an analysis of the W4 data in
order to identify relationships. Examples of modeling techniques and
other analytical techniques that could be used for analyzing the W4 data
in order to identify relationships and potential relationships has been
discussed above. The results of the analysis may include a histogram or
other data structures that represent an RWE's relationship with all of
the other known RWEs in the system or a subset of other known RWEs in the
system. Such an analysis as described above is inherently point of view
oriented in that it is from the point of view of one RWE to all of the
other RWEs known in the network.

[0092] After the analysis operation 802 any explicitly defined point of
view relationships are identified in a first identification operation
804. This operation may be done at the same time the W4 data is analyzed,
or before or after that operation 802. The first identification operation
804 may include retrieving known explicit point of view relationships
from an entity IO or may include conducting a real time search of the W4
data at the time the method 800 is performed in order to collect any new
explicit declarations of point of view relationships. A second
identification operation is also performed in which the results of the
analysis operation 802 are used in order to identify a probability score
for any probable point of view relationships based on the W4 data. For
example, as described above, if a first user has transmitted a number of
communications to a second user in which the salutation "Dear Father" or
"Dear Dad" is used, then the second identification operation 806 may,
based on this information, generate a probability score according to a
predefined algorithm which attempts to quantify the likelihood that the
recipient of the emails is the father of the sender of the emails. Such a
probability score may then be compared to a threshold, and if it exceeds
the threshold, the assumption may be made that a point of view
relationship exists between the two entities and that the point of view
relationship is that the sender is the son and the recipient is the
father. If the probability score is not high enough the system may issue
a request to clarify the nature of the relationship or may optionally do
nothing and not identify any relationship other than that there has been
contact between two people of a general nature. In the embodiment shown,
a storage operation 808 is performed in which the entity IO is updated
based on any newly identified explicit point of view relationships and
any probable point of view relationships identified in a second
identification operation 806. These point of view relationships are then
used in the data modeling of the W4 data.

[0093] In an embodiment, the method 800 is done as part of the general W4
modeling of data in order to identify relationships, and thus may be
considered a subset of the entire process of modeling relationships by
the W4 COMN. The resulting data structures will represent the
relationships as shown in FIG. 7 and allow additional analysis and
relationships to be generated based on this information.

[0094]FIG. 9 illustrates some of the elements in a W4 engine adapted to
perform W4 data optimization as described herein. The W4 engine 900
includes a correlation engine 506, an attribution engine 504 and an
attention engine 508 as described above. In addition, the W4 engine
illustrates a POV relationship identification engine 902. The POV
relationship identification engine 902 identifies relationships which are
POV relationships by inspecting explicit data provided by each RWE and by
making determinations based on probabilistic analyses of W4 data that
imply POV relationships based on analyses the correlations made by the
correlation engine 506. Upon determination that a POV relationship
exists, the POV relationship identification engine 902 stores information
in the appropriate IOs that represent RWEs associated with the
relationship.

[0095]FIG. 10 illustrates an embodiment of a method for identifying,
storing and using point of view relationships 1000. In the embodiment
shown, the FIG. 10 begins with an identification operation 1002 in which
a first RWE and a second RWE are identified that have at least one point
of view relationship between the two. In the example illustrated herein,
the nature of the relationships match those illustrated in FIG. 7. The
relationships which are identified by the first and second entity IOs may
be determined through an analysis of the W4 data as described above or
may also be determined through explicit declarations known to the W4
COMN. The rest of the identification operation equally applies to
explicit declarations of point of view relationships and implicit
identifications of point of view relationships developed based on a
probability score.

[0096] A retrieval and analysis operation 1004 is performed whenever
information identifying the relationship of the second RWE from the point
of view of the first RWE is necessary. In this embodiment, such a
relationship may be necessary when determining the first RWE's entire
list of relationships, for example when a W4 analysis in correlation has
to be performed from the first RWE's point of view. Another retrieval and
analysis operation 1006 is also shown in which the information
identifying a relationship of the first RWE from the point of view of the
second RWE is necessary. Again such analysis may be performed when an
analysis of the second RWE's relationship with all of the other RWEs
known to the system is necessary. This illustrates that this point of
view relationship is managed and stored independently from the prior
point of view relationship (i.e. that of the second RWE from the point of
view of the first).

[0097] Likewise, a third retrieval and analysis operation 1008 is
provided, wherein from a third information object may be retrieved as
necessary that identifies the relationship of the first RWE from the
point of view of the first RWE, thus, in situations where it is necessary
to determine what the first RWE's self declared relationship is or self
declared description will be retrieved in this operation 1008. Similarly,
a fourth operate retrieval and analysis operation 1010 is shown in which
from a fourth information object or data element information may be
retrieved and analyzed whenever it is necessary to determine the second
RWE's relationship from the point of view of the second RWE.

[0098] After the RWEs have been identified and analyzed, a generation
operation 1012 generates either an information object or a new data
element (i.e., entity IO) that defines each point of view relationship
identified in the identification operation 1002. In this embodiment and
alternate embodiments, a separate information object may be generated in
order to store the point of view relationship, or a data element in a
previously existing information object such as an entity object is added
or updated to contain information identifying a point of view
relationship. In operation, the alternative embodiments may be
indistinguishable and selection of either may be based on other
implementation concerns such as the amount of data used, how it was
accessed, and the number of information objects created by the system.

[0099] Those skilled in the art will recognize that the methods and
systems of the present disclosure can be implemented in many manners and
as such are not to be limited by the foregoing exemplary embodiments and
examples. In other words, functional elements being performed by single
or multiple components, in various combinations of hardware and software
or firmware, and individual functions, can be distributed among software
applications at either the client level or server level or both. In this
regard, any number of the features of the different embodiments described
herein can be combined into single or multiple embodiments, and alternate
embodiments having fewer than, or more than, all of the features
described herein are possible. Functionality can also be, in whole or in
part, distributed among multiple components, in manners now known or to
become known. Thus, myriad software/hardware/firmware combinations are
possible in achieving the functions, features, interfaces and preferences
described herein. Moreover, the scope of the present disclosure covers
conventionally known manners for carrying out the described features and
functions and interfaces, as well as those variations and modifications
that can be made to the hardware or software or firmware components
described herein as would be understood by those skilled in the art now
and hereafter.

[0100] Furthermore, the embodiments of methods presented and described as
flowcharts in this disclosure are provided by way of example in order to
provide a more complete understanding of the technology. The disclosed
methods are not limited to the operations and logical flow presented
herein. Alternative embodiments are contemplated in which the order of
the various operations is altered and in which sub-operations described
as being part of a larger operation are performed independently.

[0101] While various embodiments have been described for purposes of this
disclosure, such embodiments should not be deemed to limit the teaching
of this disclosure to those embodiments. Various changes and
modifications can be made to the elements and operations described above
to obtain a result that remains within the scope of the systems and
processes described in this disclosure. Numerous other changes can be
made that will readily suggest themselves to those skilled in the art and
which are encompassed in the spirit of the disclosure and as defined in
the appended claims.

Patent applications by Christopher William Higgins, Portland, OR US

Patent applications by Joseph James O'Sullivan, Oakland, CA US

Patent applications by Marc Eliot Davis, San Francisco, CA US

Patent applications by Ronald Martinez, San Francisco, CA US

Patent applications in class SIMULATING NONELECTRICAL DEVICE OR SYSTEM

Patent applications in all subclasses SIMULATING NONELECTRICAL DEVICE OR SYSTEM